差分进化鸟群算法的微电网多目标优化运行
薛阳1，李蕊1，张宁1，王琳2
（1 上海电力大学 自动化工程学院，上海 200090；2 国网上海市电力公司，上海 200122）
摘 要：为提高微电网在安全可靠前提下调度运行的经济性和环保性，提出了一种基于差分进化鸟群算法的微电网多目标优化运行策略。建立了考虑经济性、环保性及供电可靠性等因素的微电网多目标模型，并给出了满足微电网安全稳定运行所需的约束条件；将多目标函数转换为单目标函数，应用差分进化鸟群算法对其进行求解；将所得结果分别与各单目标下求解结果进行对比。实验结果表明，所提方法在经济性和环保性上较传统模型均有所提高，更充分利用可再生能源，降低系统运行成本，并且在负荷变动明显的情况下，系统波动性较小，一定程度上提高系统稳定性；同时该组合算法增加了种群的多样性，防止训练过程陷入局部最优解，具有效率高、鲁棒性好的优点。
关键词：微电网；多目标；优化运行；差分进化算法；鸟群算法
中图分类号：TM711 文献标识码：A 文章编号：1007-3175(2020)08-0001-06
Multi-Objective Optimal Operation of Micro-Grid Based on Differential Evolutionary Bird Swarm Algorithm
XUE Yang1, LI Rui1, ZHANG Ning 1, WANG Lin2
（1 College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China;
2 State Grid Shanghai Electric Power Company, Shanghai 200122, China）
Abstract: In order to improve the economics and environment friendly of micro-grid dispatching operation under the premise of safety and reliability, a multi-objective optimization operation strategy for micro-grid based on differential evolution bird swarm algorithm is proposed. In this paper, it established a micro-grid multi-objective model that considers factors such as economy, environment friendly, and power supply reliability, and gave the constraints required to meet the safe and stable operation of the micro-grid, the multi-objective function is converted into a single-objective function, and the differential evolution bird swarm algorithm is used to solve it; the obtained results are compared with the solution results under each single-objective. The experimental results show that the proposed method is improved in economy and environmental protection compared with the traditional model, making full use of renewable energy, reducing system operating costs, and under the condition of obvious load fluctuations, the system volatility is small, and to a certain extent, the stability of the system is improved. At the same time, the combined algorithm increases the diversity of the population and prevents the training process from falling into the local optimal solution. It has the advantages of high efficiency and good robustness.
Key words: micro-grid; multi-objective; optimal operation; differential evolutionary algorithm; bird swarm algorithm
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